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Existing table search techniques define table relatedness with unionablility and/or joinability. While these are valuable, they do not suffice for most data analysis tasks that involve numerical data, which is often aggregated over geographical, temporal, or other groups. In this demonstration, we showcase ARTS, a novel table search system centered on the unique concept of aggregate relatedness. By leveraging pre-trained language models, ARTS offers a superior column semantics understanding capability, with good labels created for both textual and numerical columns. This demonstration will offer attendees hands-on interaction with our system, revealing its potential in effectively addressing real-world data analysis challenges. 

Exploratory data analysis can uncover interesting data insights from data. Current methods utilize interestingness measures designed based on system designers' perspectives, thus inherently restricting the insights to their defined scope. These systems, consequently, may not adequately represent a broader range of user interests. Furthermore, most existing approaches that formulate interestingness measure are rule-based, which makes them inevitably brittle and often requires holistic re-design when new user needs are discovered. This paper presents a data-driven technique for deriving an interestingness measure that learns from annotated data. We further develop an innovative annotation algorithm that significantly reduces the annotation cost, and an insight synthesis algorithm based on the Markov Chain Monte Carlo method for efficient discovery of interesting insights. We consolidate these ideas into a system. Our experimental outcomes and user studies demonstrate that DAISY can effectively discover a broad range of interesting insights, thereby substantially advancing the current state-of-the-art.